This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Biology and biomedical research is very data-rich with community databases (e.g. Protein Data Bank) at several different scales. Furthermore, data are being generated at increasingly faster rates via high through-put technologies, such as micro array data, high-resolution electron microscopes, and high-resolution magnetic resonance imaging scanners. The sequence of steps needed to complete biological and biomedical research or analysis of experimental data spans from taking raw data on a specialized instrument to depositing annotated data in community databases. The complete workflow requires a scientist to bring together data from disparate resources, compare experiment with simulation, analyze data, visualize it, and then repeat some portions of this cycle. The emerging IT support paradigm for this merges grid services, workflows and data technologies. Grid computing is rapidly gaining acceptance as a routine way to transparently access computational power and datasets for biological and biomedical informatics applications. These techniques are being employed in academia as well as in industry. However, enabling biomedical codes to run in such an environment requires so-called "grid-enabling" of such codes and the associated data repositories. This process of bringing software programs, databases and instruments together to then facilitate a workflow generally involves development of a layer of software. We refer to this grid-enabling step as the "wrapping" of codes and databases. The effort in this Core Technological Research and Development project focuses on examining the grid computing requirements of several exemplary biomedical research project activities, and defining and implementing standard interfaces with appropriate security and access logging by leveraging emerging grid practice and experience. Once grid-enabled, such codes can more easily be combined together to create biomedical analysis pipelines, or workflows.